EAGER: Perceptual-Quality-Aware Video Communication in Wireless Camera Networks

EAGER：无线摄像机网络中的感知质量感知视频通信

• 批准号: 1644946
• 负责人: Rui Dai
• 金额: $ 17.81万
• 依托单位: University of Cincinnati Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-15 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1644946&HistoricalAwards=false
• 关键词: EAGER; Perceptual; Quality; Aware; Video

Recent advances in imaging hardware and wireless communications have fostered the deployment of embedded camera sensors in various wireless imaging applications, such as surveillance, smart building operations, intelligent transportation, remote health care, and consumer electronics and entertainment. It is essential to guarantee the delivery of videos generated by camera sensors to an end user with good quality. In particular, the need to maintain good perceptual quality is driven by many human-centered imaging applications, where human users rely on the received videos to make critical decisions. However, many subjective tests have shown that the perceptual quality of networked videos as evaluated by viewers cannot be merely determined by common network QoS (quality-of-service) metrics such as bit rate and packet loss ratio. Factors such as video content characteristics and compression parameters also have significant impacts on perceptual quality. The objective of this project is to achieve efficient perceptual-quality-aware video communication in wireless camera networks by jointly considering the various factors contributing to perceptual video quality. Success of the proposed research will boost user experience and enhance user's capabilities to explore and interact with the physical world through applications based on wireless camera networks.This project proposes a systematic solution for predicting and controlling perceptual video quality in wireless camera networks. The proposed research is based on: i) light-weight and accurate prediction of perceptual video quality in the network; ii) comprehensive analysis of the relationships between perceptual video quality and network parameters; iii) exploring possible ways to achieve energy-efficiency and bandwidth-efficiency by utilizing the properties of perceptual quality; and iv) designing novel rate control and scheduling protocols that can effectively control perceptual quality in dynamic network conditions. The research solutions of the project are expected to provide satisfactory perceptual video quality to users using the minimum energy and bandwidth resources in wireless camera networks. The research solutions will demonstrate how to improve user experience in wireless camera networks by utilizing the properties of perceptual video quality. The proposed research will bridge the gap between the studies in perceptual video quality and the design of wireless video communication protocols, and it will inspire new research in perceptual quality provisioning for many other wireless sensing applications.

成像硬件和无线通信的最新进展促进了嵌入式相机传感器在各种无线成像应用中的部署，例如监控、智能建筑操作、智能交通、远程医疗保健以及消费电子和娱乐。确保将摄像头传感器生成的视频以高质量交付给最终用户至关重要。特别是，保持良好的感知质量的需要是由许多以人为中心的成像应用驱动的，其中人类用户依赖于接收到的视频来做出关键决策。然而，许多主观测试已经表明，观看者所评估的网络视频的感知质量不能仅仅由诸如比特率和分组丢失率之类的常见网络QoS（服务质量）度量来确定。视频内容特性和压缩参数等因素也会对感知质量产生重要影响。该项目的目标是实现有效的感知质量感知的视频通信在无线摄像机网络中，共同考虑的各种因素有助于感知视频质量。该研究的成功将提升用户体验，并通过基于无线摄像机网络的应用程序增强用户探索和与物理世界互动的能力。本项目提出了一个系统的解决方案，用于预测和控制无线摄像机网络中的感知视频质量。本文的研究工作基于以下几个方面：（1）对网络中的感知视频质量进行轻量级而准确的预测;（2）全面分析感知视频质量与网络参数之间的关系;（3）利用感知质量的特性探索实现能量效率和带宽效率的可能途径;（4）对感知视频质量的预测方法进行深入研究。以及iv）设计新颖的速率控制和调度协议，其可以在动态网络条件下有效地控制感知质量。该项目的研究解决方案有望在无线摄像机网络中使用最少的能量和带宽资源为用户提供令人满意的感知视频质量。研究解决方案将展示如何通过利用感知视频质量的属性来改善无线摄像机网络中的用户体验。该研究将弥补感知视频质量研究与无线视频通信协议设计之间的差距，并将启发其他无线传感应用在感知质量提供方面的新研究。

项目成果

Impact of Distributed Caching on Video Streaming Quality in Information Centric Networks

分布式缓存对信息中心网络中视频流质量的影响

• DOI: 10.1109/ism.2017.80
• 发表时间: 2017
• 期刊: 2017 IEEE International Symposium on Multimedia (ISM
• 作者: Kong, Lingchao;Zhu, Jingyi;Dai, Rui;Sadat, Mohammad Nazmus
• 通讯作者: Sadat, Mohammad Nazmus

A No-Reference Image Quality Model for Object Detection on Embedded Cameras

• DOI: 10.4018/ijmdem.2019010102
• 发表时间: 2019-01
• 期刊: Int. J. Multim. Data Eng. Manag.
• 作者: Lingchao Kong;A. Ikusan;Rui Dai;Jingyi Zhu;Da Ros

Efficient Video Encoding for Automatic Video Analysis in Distributed Wireless Surveillance Systems

• DOI: 10.1145/3226036
• 发表时间: 2018-07
• 期刊: ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)
• 作者: Lingchao Kong;Rui Dai

Blind Image Quality Prediction for Object Detection

• DOI: 10.1109/mipr.2019.00046
• 发表时间: 2019-03
• 期刊: 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)
• 作者: Lingchao Kong;A. Ikusan;Rui Dai;Jingyi Zhu